{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T23:28:06Z","timestamp":1770161286334,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":43,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819557219","type":"print"},{"value":"9789819557226","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-5722-6_37","type":"book-chapter","created":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T08:14:10Z","timestamp":1769933650000},"page":"343-356","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LLMATCH: A\u00a0Unified Schema Matching Framework with\u00a0Large Language Models"],"prefix":"10.1007","author":[{"given":"Sha","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yuchen","family":"Li","sequence":"additional","affiliation":[]},{"given":"Hanhua","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Bing Tian","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Roy Ka-Wei","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Yanfei","family":"Dong","sequence":"additional","affiliation":[]},{"given":"Lambert","family":"Deng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,2]]},"reference":[{"key":"37_CR1","unstructured":"Achiam, J., et\u00a0al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)"},{"key":"37_CR2","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"632","DOI":"10.1007\/978-3-030-29908-8_50","volume-title":"PRICAI 2019: Trends in Artificial Intelligence","author":"R Ackerman","year":"2019","unstructured":"Ackerman, R., Gal, A., Sagi, T., Shraga, R.: A cognitive model of human bias in matching. In: Nayak, A.C., Sharma, A. (eds.) PRICAI 2019. LNCS (LNAI), vol. 11670, pp. 632\u2013646. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29908-8_50"},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Alwan, A.A., Nordin, A., Alzeber, M., Abualkishik, A.Z.: A survey of schema matching research using database schemas and instances. Int. J. Adv. Comput. Sci. Appl. 8(10) (2017)","DOI":"10.14569\/IJACSA.2017.081014"},{"key":"37_CR4","unstructured":"Anadkat, S.: How to make your completions outputs consistent with the new seed parameter (2023). https:\/\/cookbook.openai.com\/examples\/reproducible_outputs_with_the_seed_parameter, openAI Cookbook"},{"key":"37_CR5","doi-asserted-by":"crossref","unstructured":"Asif-Ur-Rahman, M., et al.: A semi-automated hybrid schema matching framework for vegetation data integration. Expert Syst. Appl. 229 (2023)","DOI":"10.1016\/j.eswa.2023.120405"},{"key":"37_CR6","doi-asserted-by":"crossref","unstructured":"Bernstein, P.A., Madhavan, J., Rahm, E.: Generic schema matching, ten years later. Proc. VLDB Endow. 4(11) (2011)","DOI":"10.14778\/3402707.3402710"},{"key":"37_CR7","unstructured":"Cappuzzo, R., Papotti, P., Thirumuruganathan, S.: Embdi: generating embeddings for relational data integration. In: 29th Italian Symposium on Advanced Database Systems (SEDB), Pizzo Calabro, Italy (2021)"},{"key":"37_CR8","unstructured":"Janssen Research & Development: ETL lambdabuilder documentation (2024). https:\/\/ohdsi.github.io\/ETL-LambdaBuilder\/. Accessed 01 Mar 2025"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Do, H.H., Rahm, E.: Coma\u2014a system for flexible combination of schema matching approaches. In: VLDB 2002: Proceedings of the 28th International Conference on Very Large Databases. Elsevier (2002)","DOI":"10.1016\/B978-155860869-6\/50060-3"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Feng, J., Hong, X., Qu, Y.: An instance-based schema matching method with attributes ranking and classification. In: 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, vol.\u00a05. IEEE (2009)","DOI":"10.1109\/FSKD.2009.168"},{"key":"37_CR11","doi-asserted-by":"publisher","unstructured":"Fernandez, R.C., Elmore, A.J., Franklin, M.J., Krishnan, S., Tan, C.: How large language models will disrupt data management. Proc. VLDB Endow. 16(11) (2023). https:\/\/doi.org\/10.14778\/3611479.3611527","DOI":"10.14778\/3611479.3611527"},{"key":"37_CR12","unstructured":"Fernandez, R.C., et al.: Seeping semantics: linking datasets using word embeddings for data discovery. In: 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE (2018)"},{"key":"37_CR13","unstructured":"Financial Times: Deutsche Bank struggles with fallout after huge Postbank IT migration: Regulator makes unprecedented rebuke as many clients locked out of their accounts for weeks (2025). https:\/\/www.ft.com\/content\/4138876c-5a10-46d4-b6c6-7d421cbd9df0. Accessed 01 Mar 2025"},{"key":"37_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/978-3-540-25956-5_5","volume-title":"The Semantic Web: Research and Applications","author":"F Giunchiglia","year":"2004","unstructured":"Giunchiglia, F., Shvaiko, P., Yatskevich, M.: S-match: an algorithm and an implementation of semantic matching. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 61\u201375. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-25956-5_5"},{"key":"37_CR15","unstructured":"Huang, Z., Guo, J., Wu, E.: Transform table to database using large language models. Proc. VLDB Endow. (2024). ISSN 2150"},{"key":"37_CR16","unstructured":"International Organization for Standardization: ISO 20022-1:2013 \u2013 Financial services \u2013 Universal financial industry message scheme \u2013 Part 1: Metamodel. https:\/\/www.iso.org\/standard\/55005.html. Accessed 07 May 2025"},{"key":"37_CR17","unstructured":"Koutras, C., Fragkoulis, M., Katsifodimos, A., Lofi, C.: Rema: graph embeddings-based relational schema matching. In: EDBT\/ICDT Workshops (2020)"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Koutras, C., et al.: Valentine: evaluating matching techniques for dataset discovery. In: ICDE. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00047"},{"key":"37_CR19","unstructured":"LLMatch: LLMatch code and dataset (2025). https:\/\/github.com\/knowledge-fusion\/LLMatch"},{"key":"37_CR20","unstructured":"Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with cupid. In: VLDB, vol.\u00a01 (2001)"},{"key":"37_CR21","unstructured":"Mehdi, O., Ibrahim, H., Affendey, L.: An approach for instance based schema matching with google similarity and regular expression. Int. Arab J. Inf. Technol. (IAJIT) 14(5) (2017)"},{"key":"37_CR22","unstructured":"Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm and its application to schema matching. In: Proceedings 18th International Conference on Data Engineering. IEEE (2002)"},{"key":"37_CR23","doi-asserted-by":"crossref","unstructured":"Munir, S., Khan, F., Riaz, M.A.: An instance based schema matching between opaque database schemas. In: 2014 4th International Conference on Engineering Technology and Technopreneuship (ICE2T). IEEE (2014)","DOI":"10.1109\/ICE2T.2014.7006242"},{"key":"37_CR24","unstructured":"OHDSI: Observational Health Data Sciences and Informatics (2019)"},{"key":"37_CR25","unstructured":"OpenAI: GPT-3.5 turbo fine-tuning and API updates (2023). https:\/\/openai.com\/index\/gpt-3-5-turbo-fine-tuning-and-api-updates\/. Accessed 12 Feb 2025"},{"key":"37_CR26","doi-asserted-by":"crossref","unstructured":"Overhage, J.M., Ryan, P.B., Reich, C.G., Hartzema, A.G., Stang, P.E.: Validation of a common data model for active safety surveillance research. J. Am. Med. Inform. Assoc. 19(1) (2012)","DOI":"10.1136\/amiajnl-2011-000376"},{"key":"37_CR27","doi-asserted-by":"crossref","unstructured":"Pan, Z., Yang, M., Monti, A.: Schema matching based on energy domain pre-trained language model. Energy Inform. 6(Suppl. 1) (2023)","DOI":"10.1186\/s42162-023-00277-0"},{"key":"37_CR28","unstructured":"Parciak, M., Vandevoort, B., Neven, F., Peeters, L.M., Vansummeren, S.: Schema matching with large language models: an experimental study. In: Joint Workshops at the 50th International Conference on Very Large Data Bases (VLDBW 2024) \u2014 TaDA 2024: 2nd International Workshop on Tabular Data Analysis (2024)"},{"key":"37_CR29","doi-asserted-by":"crossref","unstructured":"Paris, N., Lamer, A., Parrot, A.: Transformation and evaluation of the mimic database in the omop common data model: development and usability study. JMIR Med. Inform. 9(12) (2021)","DOI":"10.2196\/30970"},{"key":"37_CR30","doi-asserted-by":"crossref","unstructured":"Reich, C., et al.: Ohdsi standardized vocabularies\u2014a large-scale centralized reference ontology for international data harmonization. J. Am. Med. Inform. Assoc. 31(3) (2024)","DOI":"10.1093\/jamia\/ocad247"},{"key":"37_CR31","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-networks. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"37_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"460","DOI":"10.1007\/978-3-642-35176-1_29","volume-title":"The Semantic Web \u2013 ISWC 2012","author":"S Rong","year":"2012","unstructured":"Rong, S., Niu, X., Xiang, E.W., Wang, H., Yang, Q., Yu, Y.: A machine learning approach for instance matching based on similarity metrics. In: Cudr\u00e9-Mauroux, P., et al. (eds.) ISWC 2012. LNCS, vol. 7649, pp. 460\u2013475. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35176-1_29"},{"key":"37_CR33","unstructured":"Schulhoff, S., et\u00a0al.: The prompt report: a systematic survey of prompting techniques. arXiv preprint arXiv:2406.06608 (2024)"},{"key":"37_CR34","unstructured":"Sheetrit, E., Brief, M., Mishaeli, M., Elisha, O.: Rematch: retrieval enhanced schema matching with LLMs. arXiv preprint arXiv:2403.01567 (2024)"},{"key":"37_CR35","doi-asserted-by":"crossref","unstructured":"Shraga, R., Amir, O., Gal, A.: Learning to characterize matching experts. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00111"},{"key":"37_CR36","doi-asserted-by":"crossref","unstructured":"Shraga, R., Gal, A., Roitman, H.: Adnev: cross-domain schema matching using deep similarity matrix adjustment and evaluation. Proc. VLDB Endow. 13(9) (2020)","DOI":"10.14778\/3397230.3397237"},{"key":"37_CR37","doi-asserted-by":"crossref","unstructured":"Sorrentino, S., Bergamaschi, S., Gawinecki, M., Po, L.: Schema label normalization for improving schema matching. Data Knowl. Eng. 69(12) (2010)","DOI":"10.1016\/j.datak.2010.10.004"},{"key":"37_CR38","doi-asserted-by":"crossref","unstructured":"Stang, P.E., et al.: Advancing the science for active surveillance: rationale and design for the observational medical outcomes partnership. Ann. Internal Med. 153(9) (2010)","DOI":"10.7326\/0003-4819-153-9-201011020-00010"},{"key":"37_CR39","doi-asserted-by":"crossref","unstructured":"Tu, J., et al.: Unicorn: a unified multi-tasking model for supporting matching tasks in data integration. Proc. ACM Manag. Data 1(1) (2023)","DOI":"10.1145\/3588938"},{"key":"37_CR40","doi-asserted-by":"crossref","unstructured":"Wornow, D., Suh, G., Elmore, A.J., Krishnan, S., Parameswaran, A.: Automating the enterprise with foundation models. Proc. VLDB Endow. 17(12) (2024)","DOI":"10.14778\/3681954.3681964"},{"key":"37_CR41","doi-asserted-by":"crossref","unstructured":"Zhang, J., Shin, B., Choi, J.D., Ho, J.C.: Smat: an attention-based deep learning solution to the automation of schema matching. In: Advances in Databases and Information Systems: 25th European Conference, ADBIS. Springer, Cham (2021)","DOI":"10.1007\/978-3-030-82472-3_19"},{"key":"37_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Schema matching using pre-trained language models. In: International Conference on Data Engineering (ICDE). IEEE (2023)","DOI":"10.1109\/ICDE55515.2023.00123"},{"key":"37_CR43","doi-asserted-by":"crossref","unstructured":"Zhao, H., Ram, S.: Combining schema and instance information for integrating heterogeneous data sources. Data Knowl. Eng. 61(2) (2007)","DOI":"10.1016\/j.datak.2006.06.004"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5722-6_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T08:14:15Z","timestamp":1769933655000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5722-6_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819557219","9789819557226"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5722-6_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 February 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"apwebwaim2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb2025.sau.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}